Solutions
Cyber Security
Cloud Security
DevSecOps Automation
Cyber Threat Intelligence
AI & MLOps Integration
Security Operations
Incident Response & Forensics
Services
Cloud Architecture & Migration
Zero Trust & IAM Solutions
SOC-as-a-Service
Threat Detection & Response
DevOps Infrastructure Design
Penetration Testing & Red Teaming
AI-Driven Security Analytics
Industries
FinTech Cybersecurity
Healthcare Cybersecurity
E-Commerce Security
Government & Defense Security
Telecom & IoT Security
Managed Security Services
24×7 Managed SOC
Managed SIEM & SOAR
Endpoint Detection & Response
Vulnerability Management
Cloud & App Security Monitoring
Compliance Management
AI Security Lab
AI-Driven Threat Defense
Neural SOC Platform
Predictive Attack Modeling
Security Data Lake
Research & AI Lab
Threat Intelligence Reports
AI Security Models
Machine Learning for Cyber Defense
Open-Source Tools
Publications & Whitepapers
AI Innovation Center
AI Research & Development
Generative Security Models
Autonomous Threat Simulation
LLM Security Framework
Future Technology Roadmaps
Global Threat Observatory
Global Cyber Threat Map
Real-Time Attack Feed
Regional Threat Reports
Zero-Day Tracker & Alerts
AI Risk Index Dashboard
Resources
Technical Insights & Advisory
Case Studies & Solution Workshops
Security Guides & Readiness Resources
Technical Workshops & Team Enablement
Knowledge Base & Operational DocumentationWhy Do Data Quality Scores Improve While Customer Complaints Increase?
A data quality score may improve while users report worse results if the score measures completeness or format rather than correctness and usefulness. The checks may also exclude a changing source or focus on records that are easy to validate.
What to check
Compare score definitions and coverage with the affected user workflows. Review validation thresholds, source inclusion, freshness, exception rates, and recent schema changes. Trace complaint examples back to raw records and the transformations that produced them.
Recommended next step
Align checks with how the data is used, add outcome-based examples, and publish known limitations with the score. Review metric changes with data owners so a better number is not mistaken for a better dataset.
How Informatix Systems can help
Informatix Systems provides data engineering and analytics services. We can assess the affected system, trace relevant dependencies, and help plan a practical correction. Contact us with the platform involved, when the issue began, and non-sensitive error details.